#1: Workforce Description

Before you can indulge in dreams of an HR department being powered by artificial intelligence and operating at light speed, you need to take a cold hard look at your people.

Who are they?

Where are they?

Where have they been and where might they go?

These kinds of questions fall squarely in the foundational layer of descriptive analytics. It’s not necessarily sexy but without good descriptives, your whole HR analytics operation will be a total waste. I repeat, a total waste.

Said differently, in my grand hierarchy of Describe, Understand, Predict, and Change everything starts with describing your workforce.

There are numerous descriptive metrics that you might come up with but below are few that I find both informative and curiously absent in most workforce descriptions:

1. Age distributions by department area
2. Proportion of those promoted within the last 2 years (again by department)
3. Levels of turnover broken down by department, hierarchy level, and/or leadership
4. Proportion of employees that have moved internally (into a new role) within the last year see Internal Hire Rate

#2: Employee Development and Career Pathing

The field of HR talks about career development, helping employees “navigate their career”.

I think that all sounds great, but let me ask you this question: How many of the Fortune 500 HR departments actually have the capability of tracing the career paths of employees into, through, and in some cases out of the company?

If an employee asked “What role typically precedes being a manager?” or “Can you describe the historically common path to becoming a director?” would those departments be able to quickly get an actual answer based on historical data across the organization?

I’m willing to bet that few if any actually have this capability, let alone make such insights openly available to its workforce.

Yet the historical data that HR departments already have on hand provides exactly the kind of data needed. With some thoughtful data manipulation and a tool like Tableau (or a homegrown web-based approach), an updateable career pathing tool can be created internally to help employees actually look at historical patterns of movement. Such a tool not only show them the common next steps out of their current role but also the paths into their desired position.

This would also help recruiters identify the best internal talent sources and previously overlooked talent pools.

#3: Connecting Engagement with Outcomes

Employee engagement is huge focus in HR and rightfully so given its established links with turnover rates and business performance.

But many benefits of an employee engagement survey are ignored simply by failing to connect it with HR and business outcomes.

• How well does engagement predict in-store sales?
• Are engagement scores equally predictive of turnover across different areas of the business?
• What is the relationship between engagement and first-call resolution in our call center?
• Are some managers better at “creating” engaged employees than others?

Measuring engagement for the sake of engagement is not a good use of energy and resources. Supporting engagement in direct support of key business outcomes is.

HR analytics can help you bridge that gap.

#4: Measuring the Impact of Employee Initiatives

Perhaps the most important question that you could ask using an Analytics mindset would be “How would we know if this is money/time/resources well spent and how would we measure it?”

For instance, you just spent a huge stack of cash on Skillsoft to extend employee learning opportunities. Do we see any evidence that this was a good idea? Does anyone actually use it? If so, who? Why? Does it help them?

This kind of question applies not just to employee learning efforts but also engagement initiatives, wellness programs, and leadership development.

Many of the initiatives carried about by HR and across the business sound good, reasonable, and plausible but few are subject to actual analysis and evaluation.

This might be convenient for the good idea fairies and consultancies selling services but it’s a missed opportunity for businesses to stop wasting time and money on nice sounding initiatives that don’t work and focusing on the few that just might.

#5: Data Visualization and Dashboards

Numbers are one thing, but a visual story is another.

Dashboards and compelling visualizations, especially those that provide a view of critical HR indicators like turnover, top talent retention, and time to fill should be the lifeblood of any HR organization.

These might be fairly standard fare in today’s HR department but moving beyond static reports to interactive dashboards that allow HR and business leaders to interactively explore specific questions on the fly is becoming the expectation.

You’ll need an effective HR analytics function to deliver and keep pace with the analytics changes afoot across the organization.

#6 (Bonus!): Predictive Models

Predictive are models are all the rage. Fortunately in this case, I really think the right systematic modeling approach can deliver big value and insights.

I’ll introduce predictive models more systematically in future posts but in a nutshell, it just means using historical data to predict numerical or categorical outcomes. These include:

• Predicting who is most/least likely to quit
• Predicting who is most/least likely to succeed in a role
• Predicting how many days an hourly employee will miss due to illness
• Predicting who is most likely to be injured on the job
• Predicting in-store sales based on engagement, employee experience, and localized economic data

Conclusion

Asking what you can actually do with HR analytics is a key step towards establishing an effective, impactful HR analytics program. HR Analytics is still in it’s developmental stages but expectations are building.

The applications provided here are not comprehensive of course, but they should certainly be enough to get your brain moving and the ball rolling.

Some of these will be low hanging fruit, others like predictive modeling will require a bit more organizational maturity. The key is to pick a problem, find a metric, and get started. More questions will undoubtedly arise but with the right mindset you will also develop the new skills needed to answer them.